Introduction
Cytogenetic analysis is important for stratifying patients with various neoplasms. We explored the use of targeted next generation sequencing (NGS) in detecting chromosomal structural ...abnormalities or copy number variations (CNVs) in patients with myeloid neoplasms.
Methods
Plasma cell-free DNA (cfDNA) from 2821 myeloid or lymphoid neoplasm patients were collected. cfDNA was sequenced using a 275 gene panel. CNVkit software was used for analyzing and visualizing CNVs. Cytogenetic data from corresponding bone marrow (BM) samples was available on 89 myeloid samples.
Results
Of the 2821 samples, 1539 (54.5%) showed evidence of mutations consistent with the presence of neoplastic clones in circulation. Of these 1539 samples, 906 (59%) showed abnormalities associated with myeloid neoplasms and 633 (41%) with lymphoid neoplasms. Chromosomal structural abnormalities in cfDNA were detected in 146 (16%) myeloid samples and 76 (12%) lymphoid samples. Upon comparison of the myeloid samples with 89 BM patients, NGS testing was able to reliably detect chromosomal gain or loss, except for fusion abnormalities. When cytogenetic abnormalities were classified according to prognostic classes, there was a complete (100%) concordance between cfDNA NGS data and cytogenetic data.
Conclusions
This data shows that liquid biopsy using targeted NGS is reliable in detecting chromosomal structural abnormalities in myeloid neoplasms. In specific circumstances, targeted NGS may be reliable and efficient to provide adequate information without the need for BM biopsy considering broad mutation profiling can be obtained through adequate sequencing within the same test. Overall, this study supports the use of liquid biopsy for early diagnosis and monitoring of patients with myeloid neoplasms.
Demonstrating the presence of myelodysplastic syndrome (MDS)-specific molecular abnormalities can aid in diagnosis and patient management. We explored the potential of using peripheral blood (PB) ...cell-free DNA (cf-DNA) and next-generation sequencing (NGS).
We performed NGS on a panel of 14 target genes using total nucleic acid extracted from the plasma of 16 patients, all of whom had confirmed diagnoses for early MDS with blasts <5%. PB cellular DNA from the same patients was sequenced using conventional Sanger sequencing and NGS.
Deep sequencing of the cf-DNA identified one or more mutated gene(s), confirming the diagnosis of MDS in all cases. Five samples (31%) showed abnormalities in cf-DNA by NGS that were not detected by Sanger sequencing on cellular PB DNA. NGS of PB cell DNA showed the same findings as those of cf-DNA in four of five patients, but failed to show a mutation in the RUNX1 gene that was detected in one patient's cf-DNA. Mutant allele frequency was significantly higher in cf-DNA compared with cellular DNA (p = 0.008).
These data suggest that cf-DNA when analyzed using NGS is a reliable approach for detecting molecular abnormalities in MDS and should be used to determine if bone marrow aspiration and biopsy are necessary.
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e21535
Background: N TRK (NTRK1, NTRK2 or NTRK3) fusions are oncogenic drivers of various types of cancer and lead to activation of the PI3K/AKT pathway. Treatment of patients with NTRK ...fusion using TRK inhibitors, such as larotrectinib or entrectinib, is associated with high response rates ( > 75%). However these fusions are very rare in most common cancers, such as lung and colorectal cancers. We examined the expression levels of NTRK1, NTRK2, and NTRk3 in non-small cell lung cancer using targeted RNA sequencing. We also examined the effects of NTRK overexpression on PI3K/AKT pathway in non-small cell lung cancer. Methods: RNA was extracted from 160 FFPE samples of non-small cell lung cancers and sequenced using targeted next generation sequencing (NGS). The RNA sequencing was based on hybrid capture using a targeted panel of 1408 genes. The number of reads ranged from 5 to 10 million. The RNA levels were measured using Cufflinks as FPKM. Results: NTRK2 fusion was detected in one sample (0.04%). However, overexpression of NTRK
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mRNA was more common and showed bimodal expression pattern. Most of the samples (75%) had low levels (median 0.6, 6,79, and 3.17 FPKM for NTR2, NTRK2, and NTRK3, respectively) while the upper 25% of samples had significantly (P < 0.0001) higher levels (Median 2.7, 280, and 17.7 KPFM for NTRK1, NTRK2, and NTRK3, respectively). There was significant difference in expression between the three genes (P < 0.0001). The highest levels were detected in NTRK2 and lowest in NTRK1. In addition, significant variation in alternative splicing was noted in NTRK2. All cases with high expression levels (above the upper quartile) showed significant variation in alternative splicing involving almost all exons. To explore, if high expression correlates with activation of the PI3K/AKT1 pathway, we examined the correlation between high levels (above upper quartile) of NTRK2 with various genes in PI3K/AKT pathway. High levels of NTRK2 mRNA (above the upper quartile) correlated with significantly higher levels of PIK3CA (P < 0.0001) and MDM2 (P = 0.0001). There was no correlation between high expression of NTRK1 or NTRK3 and PIK3CA or MDM2 mRNA. Conclusions: Although fusions involving NTRK
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genes in lung cancer are rare, significantly high expression levels can be seen almost 25% of lung cancers. Furthermore, high expression in NTRK2 correlates with activation in the PI3K/AKT pathway. This data suggests that therapy targeting NTRK2 overexpression and/or activation of PI3K/AKT pathway in patients with high NTRK2 mRNA should be explored.
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e16061
Background: Fibroblast Growth Factor Receptors (FGFR
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) abnormalities (fusion, amplification and mutations) are common in urothelial, breast and endometrial cancers. However, ...FGFR
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have been shown to play a major role in cell proliferation, differentiation, and apoptosis in other types of cancers including colorectal (CRC) and lung cancers. We explored the value of using DNA and RNA next generation sequencing (NGS) in determining the presence of abnormalities in FGFR
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in various types of cancer. Methods: Using targeted panel and next generation sequencing (NGS), we analyzed DNA sequencing data (434 genes) in 438 Solid tumors and RNA data (1408 genes) in 160 lung cancers and 53 colorectal cancers (CRC). The expression levels of the CRC and lung cancer were also compared with expression levels of 32 cases of endometrial, urothelial and breast cancers as a group of cancers known to have high incidence. Results: The DNA data showed mutations in 85 samples and CNV in 12 samples. The detected mutations were 18% in FGFR1, 25% in FGFR2, 45% in FGFR3, and 12% in FGFR4. Only 20% of the detected mutations by NGS testing can be detected if the PCR-based FDA-approved kit was used. Analysis of the expression levels of FGFR
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mRNA in CRC and lung cancer showed highest expression in FGFR2, followed by FGFR1 then FGFR3. Expression of FGFR4 was the lowest (P < 0.0001). There was no difference between CRC and lung cancer in FGFR1 and FGFR2 mRNA, but FGFR3 was slightly higher in lung cancer as compared with CRC (P = 0.01). FGFR4 was significantly higher in CRC as compared with lung cancer (P < 0.0001). No fusion involving FGFR
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was detected in any of the tested CRC or lung cancers. Upon comparing overall expression between CRC/lung cancer with the group of cancers that are known to have high incidence of FGFR
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abnormalities (urothelial, breast, and endometrial), FGFR1 and FGFR2 mRNA were significantly lower in CRC/lung cancers (P < 0.0001 and P = 0.0002, respectively), but there was no significant difference in FGFR3. However, significant overlap is noted. In contrast, FGFR4 was significantly higher in CRC (P < 0.0001). Conclusions: This data suggests that while FGFR
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genes are overall expressed in CRC and lung, some cases may have significantly high expression of FGFR
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and perhaps these cases should be singled out for treatment with FGFR inhibitors. Furthermore, NGS testing for mutations significantly more efficient and can detect significant number of mutations that can be missed if PCR-based testing is used. NGS testing of DNA and RNA is the most appropriate testing for abnormalities in FGFR
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.
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e21534
Background: Expression level of PD-L1 mRNA as determined by next generation sequencing (NGS) is becoming more routinely available in cancer profiling. We explored the relevance ...of PD-L1 mRNA expression as detected by RNA sequencing in providing information relevant to clinical practice and compared lung cancer with colorectal cancer in activating interferon pathway. Methods: RNA was extracted from 160 non-small cell lung cancers and 53 colorectal cancers (CRC) as well as from 103 samples of various types cancers that were also analyzed for PD-L1 expression by IHC. The RNA sequencing was performed using a targeted panel of 1408 genes. IHC for PD-L1 staining was performed with the FDA-cleared diagnostic assay using the 22C3 antibody. Results: Based on comparing the number of PD-L1 IHC positive tumor cells and CD274 mRNA, significant correlation between the two methodology can be demonstrated as continuous variables (P < 0.0001) as well as when the IHC positivity is grouped as 0 to 1, 2 to 9, 10 to 49, and > = 50 (P < 0.0001). However, cancers with PD-L1 by IHC > 50% had distinctly higher levels of CD274 mRNA, while all other levels of IHC positivity showed significant overlap. Overall lung cancer showed significantly higher levels in STAT1 (P = 0.002), STAT3 (P < 0.0001), STAT4 (P = 0.005), PTPN2 (0.002) as compared with CRC. However, when only CRC cases that show PD-L1 overexpression were considered, there was no significant difference in the expression in any of these genes as compared lung cancer cases that also show PD-L1 overexpression. The only difference is that lung cancer cases with PD-L1 overexpression showed higher levels of IKBKE mRNA as compared with CRC cases with PD-L1 overexpression (P = 0.004). In addition, lung cancer overall showed higher levels of PD-L2 (P = 0.0002), PD-1 (P = 0.0002), CD8A (P < 0.0001), and CD19 (P = 0.0008), but CRC with PD-L1 overexpression showed no difference in the expression of any of these genes when compared to lung cancer cases with PD-L1 overexpression. Conclusions: While CRC shows overall significantly lower RNA levels of PD-L1, PD-L2, PD-1, CD19, CD8A and genes involved in interferon pathway, CRC cases with high expression of PD-L1 mRNA are similar to lung cancer high expressors of PD-L1. Furthermore, PD-L1 quantification using NGS testing can be used to select patient for immunotherapy and might provide valuable information on co-markers that may provide better insight to selecting patients for immunotherapy.
Diagnosis and classification of tumors is increasingly dependent on biomarkers. RNA expression profiling using next-generation sequencing provides reliable and reproducible information on the biology ...of cancer. This study investigated targeted transcriptome and artificial intelligence for differential diagnosis of hematologic and solid tumors. RNA samples from hematologic neoplasms (N = 2606), solid tumors (N = 2038), normal bone marrow (N = 782), and lymph node control (N = 24) were sequenced using next-generation sequencing using a targeted 1408-gene panel. Twenty subtypes of hematologic neoplasms and 24 subtypes of solid tumors were identified. Machine learning was used for diagnosis between two classes. Geometric mean naïve Bayesian classifier was used for differential diagnosis across 45 diagnostic entities with assigned rankings. Machine learning showed high accuracy in distinguishing between two diagnoses, with area under the curve varying between 1 and 0.841. Geometric mean naïve Bayesian algorithm was trained using 3045 samples and tested on 1415 samples, and showed correct first-choice diagnosis in 100%, 88%, 85%, 82%, 88%, 72%, and 72% of acute lymphoblastic leukemia, acute myeloid leukemia, diffuse large B-cell lymphoma, colorectal cancer, lung cancer, chronic lymphocytic leukemia, and follicular lymphoma cases, respectively. The data indicate that targeted transcriptome combined with artificial intelligence are highly useful for diagnosis and classification of various cancers. Mutation profiles and clinical information can improve these algorithms and minimize errors in diagnoses.
Introduction: Cytogenetic analysis is important for stratifying patients with various myeloid neoplasms. It has been reported that whole-genome sequencing can be used as an alternative to cytogenetic ...analysis in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). With the increasing use of liquid biopsy in the diagnosis and monitoring of patients with various types of neoplasms, we explored the potential of using liquid biopsy and next generation sequencing (NGS) in detecting chromosomal structural abnormalities or copy number variation (CNV) in patients with myeloid neoplasms. For practical approach and for capturing single nucleotide variants (SNV) and to achieve enough depth in sequencing, we used targeted sequencing for determining the chromosomal structural abnormalities in cell-free DNA (cfDNA) in patients with myeloid neoplasms.
Methods: Peripheral blood plasma samples from 144 patients with myeloid neoplasms were used to extract cfDNA for NGS testing. This included 49 patients with MDS, 31 with AML, and 64 patients with myeloproliferative neoplasms (MPN). The median age was 68.5 (range: 24-96); 56 (39%) were female. cfDNA was sequenced using 275 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth was increased to more than 1000X (after removing duplicates). CNVkit software was used for analyzing and visualizing copy number variations. All samples were confirmed to be diagnostic by showing mutations in diagnostic genes with variant allele frequency >20% or by showing diagnostic chromosomal structural abnormalities (e.g., 5q deletion in MDS, 5q- syndrome). Cytogenetic data on 35 corresponding bone marrow samples (18 AML and 17 MDS) were available for comparison.
Results: Of the 144 samples, 47 (33%) showed chromosomal structural abnormalities. In the AML group, 20 of 31 (65%) showed cytogenetic abnormalities by cfDNA testing. Of these positive AML patients, 18 (90%) (58% of total AML) had poor-risk cytogenetics. Therefore, the AML patients with normal cytogenetics or cytogenetic abnormalities other than high-risk constituted 42% of total AML patients. Of the MDS group, 11 of 49 MDS patients (22%) showed cytogenetic abnormalities by cfDNA testing, 6 of whom (54.5%) had high-risk cytogenetics. Overall, 12% of all MDS had poor-risk cytogenetics by cfDNA testing. In the MPN group, 16 of 64 (25%) showed cytogenetic abnormalities, 2 of which (12.5%) had 7q deletion (3% of all MPN); the rest (87.5%) of cytogenetic-positive MPN (22% of total MPN) had other abnormalities including 20q-, +8, 12q, 17p-, 11q-, trisomy 9, trisomy 21 and others. To compare chromosomal abnormalities as detected by cfDNA NGS testing with conventional cytogenetic analysis of corresponding bone marrow samples, we classified cytogenetic findings based on risk stratification into either intermediate-risk or poor-risk. Of the 36 cases, there was 100% concordance between cfDNA data and cytogenetic data when findings were grouped based on risk classification. Two of the conventional cytogenetic samples showed no metaphases while one showed intermediate-risk abnormalities by cfDNA NGS analysis and the second showed poor-risk cytogenetic abnormalities by cfDNA NGS analysis. These 36 cases included 16 cases with normal cytogenetics. Simple abnormalities such as 5q-, 7q-, +8 were called in identical fashion, but some other abnormalities such as derivative chromosome and marker chromosome were resolved or interpreted differently by the cfDNA NGS analysis. The NGS panel design used in this study does not cover fusion genes or chromosomal translocation, and chromosomal translocations were missed at this time.
Conclusions: This data shows that liquid biopsy using and targeted NGS is reliable in detecting chromosomal structural abnormalities in myeloid neoplasms and potentially can replace the need for conventional cytogenetic testing. While the current study was not designed to detect chromosomal translocations, a small, targeted panel of 275 genes is adequate for standard risk classification of myeloid neoplasms into intermediate or high-risk. Considering that in the same test complete mutation profiling can also be achieved along with chromosomal structural analysis, liquid biopsy in myeloid neoplasms might be considered as an efficient replacement to bone marrow biopsy in patients with myeloid neoplasms when fusion genes are not expected.
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Goy: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; OncLive Peer Exchange: Honoraria; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; LLC(Targeted Oncology): Consultancy; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Xcenda: Consultancy, Honoraria; Gilead: Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding; Rosewell Park: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Honoraria, Other; Incyte: Honoraria; Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Celgene: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Hoffman la Roche: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Michael J Hennessey Associates INC: Consultancy; Hoffman la Roche: Consultancy; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Medscape: Consultancy; Phamacyclics: Research Funding; Constellation: Research Funding; Xcenda: Consultancy; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Koprivnikar: Bristol Myers Squibb: Speakers Bureau. McCloskey: BMS: Honoraria, Speakers Bureau; COTA: Other: Equity Ownership; Takeda: Consultancy, Speakers Bureau; Pfizer: Consultancy; Novartis: Consultancy; Jazz: Consultancy, Speakers Bureau; Incyte: Speakers Bureau; Amgen: Speakers Bureau.
Introduction: Liquid biopsy has been reported to be useful in predicting residual disease in patients with diffuse large B-cell lymphoma (DLBCL). Most of the studies focused on quantifying the level ...of circulating lymphoma-specific DNA. We explored the clinical relevance of the specific mutated genes in predicting progression in patients with DLBCL.
Method: Peripheral blood samples were collected from patients with DLBCL based on their visit to clinic without other specific selection. Median age of patients is 69 (range 28-91), with 51% of the patients being male. These patients were treated on multiple protocols including R-CHOP, R-EPOCH, Magrath, HCVAD, CAR-T (#2 patients), and others. cfDNA was extracted and sequenced by next generation sequencing using 177 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth is increased to more than 2000X after removing duplicates. Low level mutations are confirmed by inspecting BAM file.
Results: A total of 86 sample from 61 patients were collected post clinical remission at different time points (median 28 weeks, range: 1-994 weeks). Of these samples, 56 (65%) from 46 patients (75%) were positive. However, 6 of these samples from 4 patients had germline mutations or mutations in TET2, ASXL1, or DNMT3A that are consistent with CHIP (clonal hematopoiesis of indeterminate potential). The remaining 50 positive samples from 42 patients had 8 repeats on the same patients collected at different time points. Comparing the 19 negative patients with the 42 positive patients post-remission, patients with residual molecular disease were significantly older than patients without residual disease (P=0.01). However, there was no significant difference between the two groups in gender, ethnic background, LDH, cell of origin classification, or TP53 positivity by IHC. Patients with residual disease showed tendency for short progression-free survival (P=0.08). Focusing on patients with specific mutations detected in the cfDNA showed that 14 (23%) patients had mutations either in TP53 or MYD88. There was no significant difference in age between these two groups or any of the other clinical variables. However, patients with TP53/MYD88 mutations had significantly shorter survival (P=0.04).
Conclusion: Post-remission residual disease as measured by circulating cfDNA is an independent predictor of potential relapse in patients with DLBCL. However, presence of it is important to determine the aggressiveness of the residual circulating clone. Residual circulating lymphoma DNA with TP53 or MYD88 mutations is a strong predictor of earlier relapse.
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Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Feldman: Alexion, AstraZeneca Rare Disease: Honoraria, Other: Study investigator. Goy: Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Honoraria, Other; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria; Acerta: Consultancy, Research Funding; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Celgene: Consultancy, Honoraria, Research Funding; Michael J Hennessey Associates INC: Consultancy; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medscape: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Genentech/Hoffman la Roche: Research Funding; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; OncLive Peer Exchange: Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xcenda: Consultancy, Honoraria; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; Rosewell Park: Consultancy; LLC(Targeted Oncology): Consultancy; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Xcenda: Consultancy; Hoffman la Roche: Consultancy; Incyte: Honoraria; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Phamacyclics: Research Funding; Constellation: Research Funding; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment.
3018
Background: Diagnosis and classification of tumors is becoming increasingly dependent on biological and molecular biomarkers. RNA expression profiling using next generation sequencing (NGS) ...provides information on various biological and molecular changes in the cancer and in the microenvironment. We explored the potential of using targeted transcriptome and artificial intelligence (AI) in the differential diagnosis and classification of various hematologic and solid tumors. Methods: RNA from hematologic neoplasms (N = 2606) and solid tumors (N = 2038) as well as normal bone marrow and lymph node control (N = 806) were sequenced by NGS using a targeted 1408-gene panel. The hematologic neoplasms included 20 different subtypes. Solid tumors included 24 different subtypes. Machine learning is used for comparing two classes at a time. Geometric Mean Naïve Bayesian (GMNB) classifier is used to provide differential diagnosis across 45 diagnostic entities with assigned ranking. Results: Machine learning showed high accuracy in distinguishing between two diagnoses with AUC varied between 1 (Sarcoma vs GIST) and 0.841 (MDS vs normal control) (examples in Table). For differential diagnosis between all 45 different diagnoses, we used 3045 samples for training the GMNB algorithm and 1415 samples for testing. Correct first choice diagnosis was obtained in 100% of ALL, 88% of AML, 85% of DLBCL, 82% of colorectal cancer, 88% of lung cancer, 72% of CLL, and 72% of follicular lymphoma. The algorithm had difficulty in typically overlapping diagnoses and diagnosed as first choice 19% of MDS, 46% of normal, and 12% of MPN. Diagnosis improved significantly when second choice was considered. Conclusions: Targeted RNA profiling with proper AI can provide highly useful tools for the pathologic diagnosis and classification of various cancers. Additional information such as mutation profile and clinical information can improve these algorithms, reduce subjectivity, and minimize errors in pathologic diagnoses. Table: see text
3047
Background: Expressed RNA can capture mutations, changes in expression levels due to methylation, and provide information on cell of origin, growth, and proliferation status. We developed an ...approach to isolate fragmented RNA from peripheral blood plasma and explored its potential to be used in liquid biopsy. Methods: Peripheral blood cfRNA was extracted from patients with neoplasms in B-cell (#105), T-cell (#16), Myeloid (#73), and from solid tumors (#44), Normal individuals (#51), and reactive post-transplant (#137). RNA was sequenced using a 1459-gene panel. Expression profile was generated using Cufflinks. Results: cfRNA levels of various solid tumor biomarkers (CA-125, CA-15-3, CEA 8, Keratin19, Keratin6A...) were significantly higher (P < 0.0001) in samples from solid tumors as compared with normal control. Similarly, cfRNA lymphoid markers (CD19, CD22, CD79A, and CD79B...) and cfRNA myeloid markers (CD33, CD14, CD117, CD56...) were all higher in B-cell lymphoid neoplasms and myeloid neoplasms, respectively (P < 0.0001), as compared with control. In evaluating the host immune system, cfRNA CD4:CD8B and CD3D:CD19 ratios in normal controls were as expected (median: 5.92 and 6.87, respectively) and were significantly lower in solid tumors (median 3.40 and 2.23, respectively, P < 0.0002). Solid tumor cfRNA showed CTLA4:CD8B ratio significantly higher in tumors than in normal (median 0.74 vs 0.19, P = 0.0001), while there was no difference in cfRNA PD-L1:CD8B ratio (median 1.45 vs 1.77, P = 0.96). Similar distinct patterns are noted for various cytokine and chemokines. cfRNA was highly predictive of diagnosis (AUC > 0.98) of solid tumors, B-cell lymphoid neoplasms, T-cell lymphoid neoplasms, and myeloid neoplasms as compared with normal control. When a specific neoplastic disease was considered against all cases including control and other neoplasms, the AUC varied between 0.77 and 0.949. Conclusions: This data shows that liquid biopsy using targeted sequencing of cfRNA in patients with various types of cancer provides comprehensive and reliable information on the neoplastic disease as well as the host. Table: see text
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